This article is part of the supplement: The ISIBM International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing (IJCBS)
Recent advances in clustering methods for protein interaction networks
1 School of Information Science and Engineering, Central South University, Changsha 410083, China
2 Department of Computer Science, Georgia State University, Atlanta, GA30303, USA
3 Rush University Cancer Center, Rush University Medical Center, Chicago, IL 60612, USA
Citation and License
BMC Genomics 2010, 11(Suppl 3):S10 doi:10.1186/1471-2164-11-S3-S10Published: 1 December 2010
The increasing availability of large-scale protein-protein interaction data has made it possible to understand the basic components and organization of cell machinery from the network level. The arising challenge is how to analyze such complex interacting data to reveal the principles of cellular organization, processes and functions. Many studies have shown that clustering protein interaction network is an effective approach for identifying protein complexes or functional modules, which has become a major research topic in systems biology. In this review, recent advances in clustering methods for protein interaction networks will be presented in detail. The predictions of protein functions and interactions based on modules will be covered. Finally, the performance of different clustering methods will be compared and the directions for future research will be discussed.